For unmanned aerial vehicles (UAV)-enabled wireless powered communication network (WPCN) system, trajectory design and resource allocation are dominate strategies for maximizing the minimum uplink throughput of the UAV. However, this is a multi-constrained optimization problem with large-scale variables, which is difficult to obtain a global optimal solution. In this paper, a novel particle filter based optimization scheme for trajectory design and resource allocation of UAV-Enabled WPCN System is proposed. To solve the multi-constrained optimization problem, we transform the optimization process into a state estimation problem of the global optimal solution, and propose an improved particle filter based optimization algorithm. Combined with the block coordinate descent (BCD) technology, the objective function with large-scale variables can be effectively optimized. In practice, the large-scale variables are divided into several parts of small-scale variables to reduce the optimization dimension. Then the variables after dimensionality reduction are iteratively optimized by the proposed particle filter based optimization algorithm in turn. We demonstrate that the success rate of the optimization scheme for optimizing the objective function can be significantly improved. Simulation results have shown that the minimum uplink throughput of the proposed scheme outperformed other related schemes.